A dynamic programming approach to synchronize train timetables

Tian, Xiaopeng; Niu, Huimin

doi:10.1177/1687814017712364

Cited by 11 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scholars have also combined different intelligent algorithms to design hybrid heuristic algorithms [30,31]. Exact solving methods primarily encompass branch-and-bound algorithms [3,10,32,33], dynamic programming [34,35], and optimization solvers [36][37][38][39]. Dual decomposition algorithms, based on dual information, decompose complex large-scale problems into simpler sub-problems that are easier to solve.…”

Section: Solution Methods Aspectmentioning

confidence: 99%

An Optimization Method for Long-Distance Train Timetables Considering Passenger Travel Time Preferences

WANG,

CHEN,

CHEN

et al. 2024

Preprint

View full text Add to dashboard Cite

With the improvement of railway networks and the increase of train services, more and more passengers are choosing rail transportation. Optimizing train timetables can effectively enhance passenger satisfaction and improve the quality of railway passenger transportation services. In this paper, we develop a method for optimizing long-distance train timetables considering passenger travel time preferences. Based on statistical analysis of departure time preferences and arrival time preferences, Gaussian functions are employed to fit these preference functions. Subsequently, a mixed-integer programming model is formulated to optimize the long-distance train timetable, aiming to maximize passenger travel time preferences and minimize train operation time, while considering constraints such as headways and the range of dwelling times at stations. To solve this mixed-integer nonlinear problem, an improved genetic algorithm incorporating parallel selection and elite retention mechanisms is designed. Finally, the proposed optimization model for long-distance train timetables is applied to a real scenario on the Beijing-Guangzhou Railway. Results indicate a 33.6% improvement in passenger travel time preferences in the optimized timetable, and a reduction in undesirable train stops during "passenger sleeping hours" from 86 occurrences to 26.

show abstract

Section: Solution Methods Aspectmentioning

confidence: 99%

An Optimization Method for Long-Distance Train Timetables Considering Passenger Travel Time Preferences

WANG,

CHEN,

CHEN

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Dynamic programming is well-known for its ability to solve constrained and non-linear problems while delivering globally optimal results. However, maintaining track of several solutions at the same time comes with a cost that rises exponentially with the number of objective functions [41][42][43]. Nevertheless, we were still able to acquire an exact solution in a short time with algorithmic design.…”

Section: Dynamic Programming Approachmentioning

confidence: 95%

Optimization of Ramp Locations along Freeways: A Dynamic Programming Approach

Chen

et al. 2022

Sustainability

View full text Add to dashboard Cite

Ramps provide entrances and exits for residents to conveniently use the freeway service. Due to the high construction cost and geometric design requirements, the decision of ramp locations involves a trade-off between multiple influencing factors, such as accessibility, safety, efficiency, construction costs, etc. This study proposed a methodology for optimizing freeway ramp placement in an effort to improve freeway accessibility. The freeway ramp locating problem was formulated as a bi-objective optimization model. Two objectives were pertinent to the reduction of total social costs: the minimization of total travel cost and minimization of total construction cost. To reflect the safety concern of ramp locations, the frequency of lane changes around the ramps and the minimum spacing between ramps were constrained. We developed an exact solution method based upon dynamic programming to solve the proposed model. Finally, a case study of the Beijing–Hong Kong–Macau Expressway within Henan Province, China, was conducted to verify the effectiveness of the proposed model and solution method.

show abstract

“…On this basis, some scholars combined different intelligent algorithms to design a hybrid heuristic algorithm [29,30]. Exact solution method mainly includes search methods such as branch and bound algorithm [31,32], dynamic programming algorithm [ 33,34 ] and optimization solver [35][36][37][38]. The essence of decomposition algorithm based on dual information is to decompose complex large-scale problems into several simple sub-problems that are easy to solve.…”

Section: Related Workmentioning

confidence: 99%

A multi-objective train timetable optimization method based on sleep period inspection

Wang,

Chen,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

For passengers traveling by the railway, a reasonable train timetable can improve passenger satisfaction efficiently, and improve the service quality of railway passenger transport. Existing related research focuses on reprogramming the train timetable from the perspective of train operating time and operating income, but how to optimize the train timetable from the rationality of passenger travel time has not been greatly resolved. Therefore, this paper proposes a multi-objective train timetable compilation method based on sleep period checking. In this system, the quantitative calculation method of passenger travel time preference is defined, and the passenger arrival time preference function and departure time preference function are fitted by Gaussian function. On this basis, a mixed integer programming model for train timetable optimization is developed. Aiming at this mixed integer nonlinear problem, an improved genetic algorithm based on parallel search mechanism is designed. After the new train timetable is generated, the feasibility of the new train timetable is checked based on the sleep period checking technique. If the new train schedule is feasible, it is indicated as the optimal train schedule. Otherwise, the same algorithm is used again to reschedule. Finally, the performance of the proposed model and algorithm is verified by an example analysis.

show abstract

A dynamic programming approach to synchronize train timetables

Cited by 11 publications

References 20 publications

An Optimization Method for Long-Distance Train Timetables Considering Passenger Travel Time Preferences

An Optimization Method for Long-Distance Train Timetables Considering Passenger Travel Time Preferences

Optimization of Ramp Locations along Freeways: A Dynamic Programming Approach

A multi-objective train timetable optimization method based on sleep period inspection

Contact Info

Product

Resources

About